Author:
Lin Che-Yu,Chen Yi-Cheng,Pang Chin Pok,Yang Tung-Han
Abstract
Ultrasound viscoelastic creep imaging (UVCI) is a newly developed technology aiming to measure the viscoelastic properties of materials. The purpose of this study is to investigate the accuracy of UVCI in measuring the viscoelastic properties of heterogeneous materials that mimic pathological lesions and normal tissues. The finite element simulation is used to investigate the measurement accuracy of UVCI on three material models, including a homogeneous material, a single-inclusion phantom, and a three-layer structure. The measurement accuracy for a viscoelastic property is determined by the difference between the simulated measurement result of that viscoelastic property and its true value defined during the simulation process. The results show that UVCI in general cannot accurately measure the true values of the viscoelastic properties of a heterogeneous material, demonstrating the need to further improve the theories and technologies relevant to UVCI to improve its measurement accuracy on tissue-like heterogeneous materials.
Publisher
Taiwan Association of Engineering and Technology Innovation
Subject
Management of Technology and Innovation,General Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Environmental Engineering,General Computer Science
Reference30 articles.
1. J. Bamber, et al., “EFSUMB Guidelines and Recommendations on the Clinical Use of Ultrasound Elastography. Part 1: Basic Principles and Technology,” Ultraschall in der Medizin-European Journal of Ultrasound, vol. 34, no. 2, pp. 169-184, April 2013.
2. T. Shiina, et al., “WFUMB Guidelines and Recommendations for Clinical Use of Ultrasound Elastography. Part 1: Basic Principles and Terminology,” Ultrasound in Medicine and Biology, vol. 41, no. 5, pp. 1126-1147, May 2015.
3. R. M. Sigrist, et al., “Ultrasound Elastography: Review of Techniques and Clinical Applications,” Theranostics, vol. 7, no. 5, pp. 1303-1329, March 2017.
4. J. Ophir, et al., “Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues,” Ultrasonic Imaging, vol. 13, no. 2, pp. 111-134, April 1991.
5. A. Diker, et al., “A Novel Application Based on Spectrogram and Convolutional Neural Network for ECG Classification,” 1st International Informatics and Software Engineering Conference, pp. 1-6, November 2019.
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